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- Understanding intuition of the Two child problem
The information that at least one is a boy, however that has been decided to make that statement, does certainly exclude the probability of two girls The information about the day is seemingly not important)
- Interpretation of Shapiro-Wilk test - Cross Validated
Failing to reject a null hypothesis is an indication that the sample you have is too small to pick up whatever deviations from normality you have - but your sample is so small that even quite substantial deviations from normality likely won't be detected However a hypothesis test is pretty much beside the point in most cases that people use a test of normality for - you actually know the
- Distinguishing between two groups in statistics and machine learning . . .
Your data may have different kinds of similarities, you may want to seek for differences between boys and girls, but the algorithm may instead find groups of poor and rich kids, or intelligent and less intelligent, right- and left-handed etc
- Change in proportion - two timepoints - Cross Validated
So that is a difference of 5% for boys and 18% for girls between the two time points: I was hoping someone could point me in the right direction as to what test I should use to compare this change (5% versus 18%) between two timepoints
- Graph for relationship between two ordinal variables
What is an appropriate graph to illustrate the relationship between two ordinal variables? A few options I can think of: Scatter plot with added random jitter to stop points hiding each other
- interpretation - Intercepts (reference) in linear mixed effect model . . .
Statistically, if I choose to use a level with the highest predicted value as the reference, it will mean that all other levels are compared to that reference Right? In that case, the results interpretation will be based on how other levels are compared or significantly differ (or not) from the reference level Would that be a good way to go about it?
- Trying to understand the fitted vs residual plot? [duplicate]
A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line This suggests that the assumption that the relationship is linear is reasonable The res
- Is this conclusion drawn from sample or population?
Population: your class is the population and you can say "boys in my class are on average 5cm higher than girls" This would be statement of fact after measuring the heights No confidence intervals, p-values or other uncertainty estimates would be needed Sample: your class is a sample of all the boys and girls that could possibly attend your
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